An Estimation of Missing Values by Modified Mixed Kernels

----In statistical practices, difficulties of missing data are universal. Several techniques are used to handle this dilemma of missing data. They include both old approaches, which require only a small amount of mathematical computations and new approaches, which require additional difficult computations that are ever easier for social work researchers to carry out the statistical programming softwares. In the existing system, there is a novel setting of missing data imputation, i.e. imputing in mixed-attribute data sets. This system offers two consistent estimators for discrete and continuously missing target values, correspondingly. After that a mixture-kernel based iterative estimator is offered to impute mixed-attribute data sets. In this method, the local kernel and global kernel are used and linear combination of these mixed kernels is used. Nevertheless, the accuracy of the system is decreased with the large number of data samples. Unquestionably it will degrade the performance of the system. To improve the performance and to increase the accuracy of the system we proposed three approaches. First we introduce the local kernal RBF using KL divergence, secondly we introduce the global kernal polynomial using probability distribution and finally mixed kernels in piece level combination instead of linear combination. From the experimental result we can obtain that the proposed system is much more effective than the existing system. The performance also is shown to have improved in this proposed system. Keywords----Missing data imputation, Kernel Function Selection, Linear Mixture Kernel Function, RBF kernel, Polynomial kernel and Statistical Imputation for Missing Data.